In this paper we present a dual, wide area, collaborative augmented reality (AR) system that consists of standard live view augmentation, e.g., from helmet, and zoomed-in view augmentation, e.g., from binoculars. The proposed advanced scouting capability allows long range high precision augmentation of live unaided and zoomed-in imagery with aerial and terrain based synthetic objects, vehicles, people and effects. The inserted objects must appear stable in the display and not jitter or drift as the user moves around and examines the scene. The AR insertions for the binocs must work instantly when they are picked up anywhere as the user moves around. The design of both AR modules is based on using two different cameras with wide and narrow field of view (FoV) lenses. The wide FoV gives context and enables the recovery of location and orientation of the prop in 6 degrees of freedom (DoF) much more robustly, whereas the narrow FoV is used for the actual augmentation and increased precision in tracking. Furthermore, narrow camera in unaided eye and wide camera on the binoculars are jointly used for global yaw (heading) correction. We present our navigation algorithms using monocular cameras in combination with IMU and GPS in an Extended Kalman Filter (EKF) framework to obtain robust and real-time pose estimation for precise augmentation and cooperative tracking.

This paper introduces a user-worn Augmented Reality (AR) based first-person weapon shooting system (AR-Weapon), suitable for both training and gaming. Different from existing AR-based first-person shooting systems, AR-Weapon does not use fiducial markers placed in the scene for tracking. Instead it uses natural scene features observed by the tracking camera from the live view of the world. The AR-Weapon system estimates 6-degrees of freedom orientation and location of the weapon and of the user operating it, thus allowing the weapon to fire simulated projectiles for both direct fire and non-line of sight during live runs. In addition, stereo cameras are used to compute depth and provide dynamic occlusion reasoning. Using the 6-DOF head and weapon tracking, dynamic occlusion reasoning and a terrain model of the environment, the fully virtual projectiles and synthetic avatars are displayed on the user's head mounted Optical-See-Through (OST) display overlaid over the live view of the real world. Since the projectiles, weapon characteristics and virtual enemy combatants are all simulated they can easily be changed to vary scenarios, new projectile types and future weapons. In this paper, we present the technical algorithms, system design and experiment results for a prototype AR-Weapon system.

In this paper we present an augmented reality binocular system to allow long range high precision augmentation of live telescopic imagery with aerial and terrain based synthetic objects, vehicles, people and effects. The inserted objects must appear stable in the display and must not jitter and drift as the user pans around and examines the scene with the binoculars. The design of the system is based on using two different cameras with wide field of view and narrow field of view lenses enclosed in a binocular shaped shell. Using the wide field of view gives us context and enables us to recover the 3D location and orientation of the binoculars much more robustly, whereas the narrow field of view is used for the actual augmentation as well as to increase precision in tracking. We present our navigation algorithm that uses the two cameras in combination with an inertial measurement unit and global positioning system in an extended Kalman filter and provides jitter free, robust and real-time pose estimation for precise augmentation. We have demonstrated successful use of our system as part of information sharing example as well as a live simulated training system for observer training, in which fixed and rotary wing aircrafts, ground vehicles, and weapon effects are combined with real world scenes.

In this paper, we expand our previous work on augmented reality (AR) binoculars to support wider range of user motion - up to thousand square meters compared to only a few square meters as before. We present our latest improvements and additions to our pose estimation pipeline and demonstrate stable registration of objects on the real world scenery while the binoculars are undergoing significant amount of parallax-inducing translation.

AR-Mentor is a wearable real time Augmented Reality (AR) mentoring system that is configured to assist in maintenance and repair tasks of complex machinery, such as vehicles, appliances, and industrial machinery. The system combines a wearable Optical-See-Through (OST) display device with high precision 6-Degree-Of-Freedom (DOF) pose tracking and a virtual personal assistant (VPA) with natural language, verbal conversational interaction, providing guidance to the user in the form of visual, audio and locational cues. The system is designed to be heads-up and hands-free allowing the user to freely move about the maintenance or training environment and receive globally aligned and context aware visual and audio instructions (animations, symbolic icons, text, multimedia content, speech). The user can interact with the system, ask questions and get clarifications and specific guidance for the task at hand. A pilot application with AR-Mentor was successfully built to instruct a novice to perform an advanced 33-step maintenance task on a training vehicle. The initial live training tests demonstrate that AR-Mentor is able to help and serve as an assistant to an instructor, freeing him/her to cover more students and to focus on higher-order teaching.

In this paper we present an augmented reality binocular system to allow long range high precision augmentation of live telescopic imagery with aerial and terrain based synthetic objects, vehicles, people and effects. The inserted objects must appear stable in the display and must not jitter and drift as the user pans around and examines the scene with the binoculars. The design of the system is based on using two different cameras with wide field of view, and narrow field of view lenses enclosed in a binocular shaped shell. Using the wide field of view gives us context and enables us to recover the 3D location and orientation of the binoculars much more robustly, whereas the narrow field of view is used for the actual augmentation as well as to increase precision in tracking. We present our navigation algorithm that uses the two cameras in combination with an IMU and GPS in an Extended Kalman Filter (EKF) and provides jitter free, robust and real-time pose estimation for precise augmentation. We have demonstrated successful use of our system as part of a live simulated training system for observer training, in which fixed and rotary wing aircrafts, ground vehicles, and weapon effects are combined with real world scenes.

In this paper, we present a high speed optical motion capture method that can measure three dimensional motion, orientation, and incident illumination at tagged points in a scene. We use tracking tags that work in natural lighting conditions and can be imperceptibly embedded in attire or other objects. Our system supports an unlimited number of tags in a scene, with each tag uniquely identified to eliminate marker reacquisition issues. Our tags also provide incident illumination data which can be used to match scene lighting when inserting synthetic elements. The technique is therefore ideal for on-set motion capture or real-time broadcasting of virtual sets. Unlike previous methods that employ high speed cameras or scanning lasers, we capture the scene appearance using the simplest possible optical devices - a light-emitting diode (LED) with a passive binary mask used as the transmitter and a photosensor used as the receiver. We strategically place a set of optical transmitters to spatio-temporally encode the volume of interest. Photosensors attached to scene points demultiplex the coded optical signals from multiple transmitters, allowing us to compute not only receiver location and orientation but also their incident illumination and the reflectance of the surfaces to which the photosensors are attached. We use our untethered tag system, called Prakash, to demonstrate methods of adding special effects to captured videos that cannot be accomplished using pure vision techniques that rely on camera images.

This paper presents a new approach to imaging that significantly enhances the dynamic range of a camera. The key idea is to adapt the exposure of each pixel on the image detector, based on the radiance value of the corresponding scene point. This adaptation is done in the optical domain, that is, during image formation. In practice, this is achieved using a spatial light modulator whose transmittance can be varied with high resolution over space and time. A real-time control algorithm is developed that uses acquired images to automatically adjust the transmittance function of the spatial modulator. Each captured image and its corresponding transmittance function are used to compute a very high dynamic range image that is linear in scene radiance. We have implemented a video-rate adaptive dynamic range camera that consists of a color CCD detector and a controllable liquid crystal light modulator. Experiments have been conducted in scenarios with complex and harsh lighting conditions. The results indicate that adaptive imaging can have a significant impact on vision applications such as monitoring, tracking, recognition, and navigation.